Chemical array technologies have gained prominence in biological research and are likely to become important and widely used diagnostic tools in the healthcare industry. Currently, chemical-array techniques are most often used to determine the concentrations of particular nucleic-acid polymers in complex sample solutions. Chemical-array-based analytical techniques are not, however, restricted to analysis of nucleic acid solutions, but may be employed to analyze complex solutions of any type of molecule that can be optically or radiometrically scanned and that can bind with high specificity to complementary molecules synthesized within, or bound to, discrete features on the surface of a chemical array.
Scanning of a feature by an optical scanning device or radiometric scanning device generally produces a scanned image comprising a rectilinear grid of pixels, with each pixel having a corresponding signal intensity. It is desirable for the signal intensities, or counts, of pixels within the area of a pixel-based scanned image corresponding to a feature to be relatively uniform. Similarly, it is also desirable for the signal intensities within background regions surrounding features to be relatively uniform. Non-uniform signal intensity distributions generally indicate the occurrence of one or more error or noise conditions that may prevent meaningful data from being collected from the feature.
One current technique for identifying outlier features, or feature backgrounds, involves manual inspection of scanned images and manual flagging of those features or feature backgrounds that appear to be nonuniform, as visually identified by a user/inspector. Additionally, the manual flags may be noted in feature extraction software that further processes the feature and/or background signals, so that the feature extraction software ignores those features and feature backgrounds that are flagged, and does not process the signals therefrom. This technique is very time consuming, tedious, and prone to subjective error, as standards for flagging may vary from user to user.
Another method of identifying outlier features or outlier feature backgrounds is described in U.S. Pat. No. 6,832,163, which is hereby incorporated herein, in its entirety, by reference thereto. This method automatically flags outlier features and outlier local backgrounds if the pixel variance of that feature or local background is determined to be outside of a calculated limit. A toggle parameter is determined from low signal features on the array and a variance limit is calculated from an equation that estimates a pixel model error. The equation that estimates a pixel model error, and includes two terms: a constant term derived from the low signal probes, and a coefficient of variation term that is tuned. Tuning is a process performed by internal developers using many arrays from different lots and experimenters. The constant term is tuned by examining the pixel standard deviation (SD) of low signal features. The coefficient of variation term is a variable term that is optimized by changing a multiplier of the term and then examining resultant output data iteratively as the multiplier is varied each time. The coefficient of variation term is tuned by observing the pixel CV of features (standard deviation of pixel signals/net signal) at the high signal range.
Another method of identifying outlier features or outlier feature backgrounds is described in U.S. Pat. No. 6,993,172, which is hereby incorporated herein, in its entirety, by reference thereto. In this method, the pixel noise or error model has three terms or coefficients, which provide an additional degree of freedom over that described in U.S. Pat. No. 6,832,163 and may consequently tend to fit the pixel data better. The pixel noise model includes a constant term (C), a Poissonian term (B) and a coefficient of variation (CV) term (A).
The methods described in U.S. Pat. Nos. 6,832,163 and 6,993,172 can be very time consuming, particularly for internal developers, as each array platform must be tuned individually. Variations that occur in array platforms that require re-tuning of the model terms of these methods include different applications of the array (e.g., gene expression versus CGH analysis or location analysis); differences in upstream wet protocols, such as variations in labeling, hybridization or washing of arrays; different scanner manufacturers or models, variable photomultiplier tube settings for different channels for the same array, variable photomultiplier tube settings for different arrays; and variable photomultiplier tube settings for different channels on different scanners.
Thus, designers, manufacturers, and users of chemical arrays have recognized the need for more automated methods for recognizing outlier features and outlier feature backgrounds in scanned images of chemical arrays, that require less setup and tuning than what is currently available.